Connecting an AI model to your systems — your CRM, your files, your database, GitHub, Slack — used to require a custom integration for each combination. With many tools and many models, that becomes unmanageable. MCP (Model Context Protocol) was created to solve exactly that: a single standard so any AI application can talk to any data source or tool.

What is MCP?

The Model Context Protocol is an open standard — introduced by Anthropic in late 2024 and widely adopted by the industry — that defines a common way to connect AI assistants with external data and tools. The most-used analogy: MCP is like the “USB-C of AI”. Instead of a different cable for each device, a single standardized connector works for everything.

Before MCP, connecting M tools with N models required M×N integrations. With MCP, each tool and each model implements the standard once: the problem goes from M×N to M+N.

Components: the MCP architecture

MCP works with three pieces that communicate through JSON-RPC messages:

  • Host: the AI application the user interacts with (a desktop assistant, an IDE, an enterprise chatbot). It contains the model.
  • Client: lives inside the host and keeps a one-to-one connection with a server. It translates between host and server.
  • Server: a lightweight program that exposes a specific capability — access to your database, to GitHub, to the file system — following the standard.

A single host can connect to several servers at once (one for your email, another for your database, another for your repository).

What an MCP server exposes

Each server can offer three kinds of “primitives”:

  • Tools: functions the model can execute — create a ticket, query inventory, send a message. They enable actions, not just reading.
  • Resources: data and context the model can read — a file, a database record, a page.
  • Prompts: reusable templates that standardize frequent tasks.

Communication can be local (server on the same machine) or remote (server in the cloud, over HTTP), which supports both desktop integrations and enterprise services.

What is it used for?

MCP is the “plumbing” that turns an AI assistant into something connected to your real operation. Typical use cases:

  • Letting an assistant query and update your CRM or ERP.
  • Connecting AI to GitHub to review code, open issues or read a repository.
  • Giving secure access to internal documents, databases or ticketing systems.
  • Building agents that take actions across several systems through one standard interface.

Why it matters for your company

MCP dramatically lowers the cost of integrating AI with your systems and avoids vendor lock-in: because it’s an open standard, what you connect today works with different models tomorrow. For an organization, that means adopting AI on the infrastructure it already has, in a secure and maintainable way.

Keep reading: What is RAG? · MCP vs RAG.

At Grupo TANDEM we help companies connect AI to their real systems securely. If you want to explore what you could automate, let’s talk.